def _default_event_space_bijector(self):
# TODO(b/145620027) Finalize choice of bijector.
return chain_bijector.Chain([
exp_bijector.Log(validate_args=self.validate_args),
softmax_centered_bijector.SoftmaxCentered(
validate_args=self.validate_args),
], validate_args=self.validate_args)
def _default_event_space_bijector(self):
# TODO(b/145620027) Finalize choice of bijector.
return chain_bijector.Chain([
invert_bijector.Invert(
square_bijector.Square(validate_args=self.validate_args),
validate_args=self.validate_args),
softmax_centered_bijector.SoftmaxCentered(
validate_args=self.validate_args)
], validate_args=self.validate_args)
def _compute_quantiles():
"""Helper to build quantiles."""
# Omit {0, 1} since they might lead to Inf/NaN.
zero = tf.zeros([], dtype=dist.dtype)
edges = tf.linspace(zero, 1., quadrature_size + 3)[1:-1]
# Expand edges so its broadcast across batch dims.
edges = tf.reshape(
edges,
shape=tf.concat(
[[-1], tf.ones([batch_ndims], dtype=tf.int32)], axis=0))
quantiles = dist.quantile(edges)
quantiles = softmax_centered_bijector.SoftmaxCentered().forward(quantiles)
# Cyclically permute left by one.
perm = tf.concat([tf.range(1, 1 + batch_ndims), [0]], axis=0)
quantiles = tf.transpose(quantiles, perm)
quantiles.set_shape(_get_final_shape(quadrature_size + 1))
return quantiles
def _default_event_space_bijector(self):
# TODO(b/145620027) Finalize choice of bijector.
return softmax_centered_bijector.SoftmaxCentered(
validate_args=self.validate_args)
def _default_event_space_bijector(self):
return softmax_centered_bijector.SoftmaxCentered(
validate_args=self.validate_args)
